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azure-media-videoanalyzer-edge from group com.azure (version 1.0.0-beta.6)
Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.
Group: com.azure Artifact: azure-media-videoanalyzer-edge
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Artifact azure-media-videoanalyzer-edge
Group com.azure
Version 1.0.0-beta.6
Last update 30. April 2022
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies azure-core,
There are maybe transitive dependencies!
Group com.azure
Version 1.0.0-beta.6
Last update 30. April 2022
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies azure-core,
There are maybe transitive dependencies!
rulelearner from group com.rulelearner (version 8.4.1.1)
Rule Learner is an open source Java library for learning business rules from
historical data. It supports supervised Machine Learning by incorporating well-known
algorithms such as RIPPER and C4.5 provided by open source tools such as Weka.
Rule Learner is implemented as a Machine Learning component of OpenRules Decision Manager.
It allows business analysts to place their historical data in simple Excel tables and
to automatically discover business rules that find certain pattern in this data.
The generated rules are human readable and presented in Excel format executable by
OpenRules Decision Manager.
0 downloads
Artifact rulelearner
Group com.rulelearner
Version 8.4.1.1
Last update 30. September 2021
Organization not specified
URL http://rulelearner.com
License GNU Lesser General Public License (LGPL)
Dependencies amount 5
Dependencies openrules-core, log4j-slf4j-impl, weka-stable, poi, poi-ooxml,
There are maybe transitive dependencies!
Group com.rulelearner
Version 8.4.1.1
Last update 30. September 2021
Organization not specified
URL http://rulelearner.com
License GNU Lesser General Public License (LGPL)
Dependencies amount 5
Dependencies openrules-core, log4j-slf4j-impl, weka-stable, poi, poi-ooxml,
There are maybe transitive dependencies!
sbscl from group org.draegerlab (version 2.1)
The Systems Biology Simulation Core Library provides an efficient and
exhaustive Java™ implementation of methods to interpret the content of
models encoded in the Systems Biology Markup Language (SBML) and its
numerical solution. This library is based on the JSBML project. It can be
used on every operating system for which a Java Virtual Machine is available.
Version 2.0 and beyond support simulation in three frameworks:
constraint-based analysis, stochastic simulation, and ordinary differential
equation systems. SBSCL supports SED-ML and COMBINE archives in OMEX format.
Artifact sbscl
Group org.draegerlab
Version 2.1
Last update 20. April 2021
Organization not specified
URL https://github.com/draeger-lab/SBSCL/
License GNU Lesser General Public License
Dependencies amount 14
Dependencies junit, jsbml, jdom2, jmathml, jlibsedml, commons-math, commons-lang3, jfreechart, colt, SCPSolver, GLPKSolverPack, LPSOLVESolverPack, libkisao, CombineArchive,
There are maybe transitive dependencies!
Group org.draegerlab
Version 2.1
Last update 20. April 2021
Organization not specified
URL https://github.com/draeger-lab/SBSCL/
License GNU Lesser General Public License
Dependencies amount 14
Dependencies junit, jsbml, jdom2, jmathml, jlibsedml, commons-math, commons-lang3, jfreechart, colt, SCPSolver, GLPKSolverPack, LPSOLVESolverPack, libkisao, CombineArchive,
There are maybe transitive dependencies!
jart from group io.jart (version 0.0.4)
JARTs are Java-based Asynchronous Real Time sockets.
JART uses JNA to build a pure Java TCP/IP stack upon Netmap. It is implemented in imperative style (no TCP state machine) using asynchronous programming with the help of ea-async.
JART runs on both Linux (with the proper Netmap kernel module) and FreeBSD with Netmap enabled. FreeBSD 12.1+ has Netmap in the kernel by default so it works "out of the box".
While JART has proven fairly robust in limited testing, it is still a work-in-progress and may not be suitable for production use. Pull requests welcome!
Artifact jart
Group io.jart
Version 0.0.4
Last update 06. May 2020
Organization not specified
URL https://github.com/scott-jart-io/jart
License BSD 3-Clause License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group io.jart
Version 0.0.4
Last update 06. May 2020
Organization not specified
URL https://github.com/scott-jart-io/jart
License BSD 3-Clause License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
ordinalClassClassifier from group nz.ac.waikato.cms.weka (version 1.0.5)
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see:
Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001.
Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.
Group: nz.ac.waikato.cms.weka Artifact: ordinalClassClassifier
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1 downloads
Artifact ordinalClassClassifier
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 06. December 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ordinalClassClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 06. December 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ordinalClassClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
ujmp from group org.ujmp (version 0.3.0)
The Universal Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations and
linear algebra in Java. In addition to the basic operations like matrix multiplication, matrix inverse or
decomposition methods, it also supports visualization, JDBC import/export and many other useful functions such
as mean, correlation, standard deviation, mutual information, or the replacement of missing values.
It's a swiss army knife for data processing in Java, tailored to machine learning applications.
Group: org.ujmp Artifact: ujmp
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Artifact ujmp
Group org.ujmp
Version 0.3.0
Last update 30. July 2015
Organization Universal Java Matrix Package
URL https://ujmp.org/
License GNU LESSER GENERAL PUBLIC LICENSE
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.ujmp
Version 0.3.0
Last update 30. July 2015
Organization Universal Java Matrix Package
URL https://ujmp.org/
License GNU LESSER GENERAL PUBLIC LICENSE
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
consistencySubsetEval from group nz.ac.waikato.cms.weka (version 1.0.4)
Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.
Group: nz.ac.waikato.cms.weka Artifact: consistencySubsetEval
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1 downloads
Artifact consistencySubsetEval
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 16. October 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/consistencySubsetEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 16. October 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/consistencySubsetEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
dagging from group nz.ac.waikato.cms.weka (version 1.0.3)
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier.
Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data.
For more information, see:
Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.
2 downloads
Artifact dagging
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/dagging
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/dagging
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
userClassifier from group nz.ac.waikato.cms.weka (version 1.0.3)
Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit.
For more information see:
Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.
Group: nz.ac.waikato.cms.weka Artifact: userClassifier
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2 downloads
Artifact userClassifier
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 25. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/userClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 25. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/userClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
averagedOneDependenceEstimators from group nz.ac.waikato.cms.weka (version 1.2.1)
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
Group: nz.ac.waikato.cms.weka Artifact: averagedOneDependenceEstimators
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Artifact averagedOneDependenceEstimators
Group nz.ac.waikato.cms.weka
Version 1.2.1
Last update 20. July 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.2.1
Last update 20. July 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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